Drawing human pedigree charts with DrawPed.

Creating pedigree charts is a recurring task in biomedical research, but there are few online tools for drawing complex human pedigrees available and even fewer are free. With DrawPed we aim to close this gap. DrawPed automatically draws pedigree charts from standard PED format pedigree files. Users can also create pedigrees from scratch and interactively edit existing pedigrees. The application can display conditions not captured in a PED file such as deceased persons or suspected consanguinity of parents. Pedigree charts are displayed as SVGs, which are scalable and hence publication-ready. Pedigrees can be exported as PED files for storage, exchange, or use in other applications. DrawPed is open source and freely available at https://www.genecascade.org/DrawPed/.

REEV: review, evaluate and explain variants.

In the era of high throughput sequencing, special software is required for the clinical evaluation of genetic variants. We developed REEV (Review, Evaluate and Explain Variants), a user-friendly platform for clinicians and researchers in the field of rare disease genetics. Supporting data was aggregated from public data sources. We compared REEV with seven other tools for clinical variant evaluation. REEV (semi-)automatically fills individual ACMG criteria facilitating variant interpretation. REEV can store disease and phenotype data related to a case to use these for phenotype similarity measures. Users can create public permanent links for individual variants that can be saved as browser bookmarks and shared. REEV may help in the fast diagnostic assessment of genetic variants in a clinical as well as in a research context. REEV (https://reev.bihealth.org/) is free and open to all users and there is no login requirement.

Loss-of-function variants affecting the STAGA complex component SUPT7L cause a developmental disorder with generalized lipodystrophy.

Generalized lipodystrophy is a feature of various hereditary disorders, often leading to a progeroid appearance. In the present study we identified a missense and a frameshift variant in a compound heterozygous state in SUPT7L in a boy with intrauterine growth retardation, generalized lipodystrophy, and additional progeroid features. SUPT7L encodes a component of the transcriptional coactivator complex STAGA. By transcriptome sequencing, we showed the predicted missense variant to cause aberrant splicing, leading to exon truncation and thereby to a complete absence of SUPT7L in dermal fibroblasts. In addition, we found altered expression of genes encoding DNA repair pathway components. This pathway was further investigated and an increased rate of DNA damage was detected in proband-derived fibroblasts and genome-edited HeLa cells. Finally, we performed transient overexpression of wildtype SUPT7L in both cellular systems, which normalizes the number of DNA damage events. Our findings suggest SUPT7L as a novel disease gene and underline the link between genome instability and progeroid phenotypes.

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks.

BACKGROUND: Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks consequently provide higher accuracies. However, it is unclear whether such approaches can uncover novel non-canonical, hitherto unexpected TFBSs relevant to human transcriptional regulation. RESULTS: In this study, we trained a convolutional recurrent neural network with HT-SELEX data for GRHL1 binding and applied it to a set of GRHL1 binding sites obtained from ChIP-Seq experiments from human cells. We identified 46 non-canonical GRHL1 binding sites, which were not found by a conventional PWM approach. Unexpectedly, some of the newly predicted binding sequences lacked the CNNG core motif, so far considered obligatory for GRHL1 binding. Using isothermal titration calorimetry, we experimentally confirmed binding between the GRHL1-DNA binding domain and predicted GRHL1 binding sites, including a non-canonical GRHL1 binding site. Mutagenesis of individual nucleotides revealed a correlation between predicted binding strength and experimentally validated binding affinity across representative sequences. This correlation was neither observed with a PWM-based nor another deep learning approach. CONCLUSIONS: Our results show that convolutional recurrent neural networks may uncover unanticipated binding sites and facilitate quantitative transcription factor binding predictions.

Alternative splicing is coupled to gene expression in a subset of variably expressed genes.

Numerous factors regulate alternative splicing of human genes at a co-transcriptional level. However, how alternative splicing depends on the regulation of gene expression is poorly understood. We leveraged data from the Genotype-Tissue Expression (GTEx) project to show a significant association of gene expression and splicing for 6874 (4.9%) of 141,043 exons in 1106 (13.3%) of 8314 genes with substantially variable expression in ten GTEx tissues. About half of these exons demonstrate higher inclusion with higher gene expression, and half demonstrate higher exclusion, with the observed direction of coupling being highly consistent across different tissues and in external datasets. The exons differ with respect to sequence characteristics, enriched sequence motifs, RNA polymerase II binding, and inferred transcription rate of downstream introns. The exons were enriched for hundreds of isoform-specific Gene Ontology annotations, suggesting that the coupling of expression and alternative splicing described here may provide an important gene regulatory mechanism that might be used in a variety of biological contexts. In particular, higher inclusion exons could play an important role during cell division.

Broadening the phenotypic and molecular spectrum of FINCA syndrome: Biallelic NHLRC2 variants in 15 novel individuals.

FINCA syndrome [MIM: 618278] is an autosomal recessive multisystem disorder characterized by fibrosis, neurodegeneration and cerebral angiomatosis. To date, 13 patients from nine families with biallelic NHLRC2 variants have been published. In all of them, the recurrent missense variant p.(Asp148Tyr) was detected on at least one allele. Common manifestations included lung or muscle fibrosis, respiratory distress, developmental delay, neuromuscular symptoms and seizures often followed by early death due to rapid disease progression.Here, we present 15 individuals from 12 families with an overlapping phenotype associated with nine novel NHLRC2 variants identified by exome analysis. All patients described here presented with moderate to severe global developmental delay and variable disease progression. Seizures, truncal hypotonia and movement disorders were frequently observed. Notably, we also present the first eight cases in which the recurrent p.(Asp148Tyr) variant was not detected in either homozygous or compound heterozygous state.We cloned and expressed all novel and most previously published non-truncating variants in HEK293-cells. From the results of these functional studies, we propose a potential genotype-phenotype correlation, with a greater reduction in protein expression being associated with a more severe phenotype.Taken together, our findings broaden the known phenotypic and molecular spectrum and emphasize that NHLRC2-related disease should be considered in patients presenting with intellectual disability, movement disorders, neuroregression and epilepsy with or without pulmonary involvement.

GA4GH Phenopackets: A Practical Introduction.

The Global Alliance for Genomics and Health (GA4GH) is developing a suite of coordinated standards for genomics for healthcare. The Phenopacket is a new GA4GH standard for sharing disease and phenotype information that characterizes an individual person, linking that individual to detailed phenotypic descriptions, genetic information, diagnoses, and treatments. A detailed example is presented that illustrates how to use the schema to represent the clinical course of a patient with retinoblastoma, including demographic information, the clinical diagnosis, phenotypic features and clinical measurements, an examination of the extirpated tumor, therapies, and the results of genomic analysis. The Phenopacket Schema, together with other GA4GH data and technical standards, will enable data exchange and provide a foundation for the computational analysis of disease and phenotype information to improve our ability to diagnose and conduct research on all types of disorders, including cancer and rare diseases.

RegEl corpus: identifying DNA regulatory elements in the scientific literature.

High-throughput technologies led to the generation of a wealth of data on regulatory DNA elements in the human genome. However, results from disease-driven studies are primarily shared in textual form as scientific articles. Information extraction (IE) algorithms allow this information to be (semi-)automatically accessed. Their development, however, is dependent on the availability of annotated corpora. Therefore, we introduce RegEl (Regulatory Elements), the first freely available corpus annotated with regulatory DNA elements comprising 305 PubMed abstracts for a total of 2690 sentences. We focus on enhancers, promoters and transcription factor binding sites. Three annotators worked in two stages, achieving an overall 0.73 F1 inter-annotator agreement and 0.46 for regulatory elements. Depending on the entity type, IE baselines reach F1-scores of 0.48-0.91 for entity detection and 0.71-0.88 for entity normalization. Next, we apply our entity detection models to the entire PubMed collection and extract co-occurrences of genes or diseases with regulatory elements. This generates large collections of regulatory elements associated with 137 870 unique genes and 7420 diseases, which we make openly available. Database URL: https://zenodo.org/record/6418451#.YqcLHvexVqg.

FABIAN-variant: predicting the effects of DNA variants on transcription factor binding.

While great advances in predicting the effects of coding variants have been made, the assessment of non-coding variants remains challenging. This is especially problematic for variants within promoter regions which can lead to over-expression of a gene or reduce or even abolish its expression. The binding of transcription factors to the DNA can be predicted using position weight matrices (PWMs). More recently, transcription factor flexible models (TFFMs) have been introduced and shown to be more accurate than PWMs. TFFMs are based on hidden Markov models and can account for complex positional dependencies. Our new web-based application FABIAN-variant uses 1224 TFFMs and 3790 PWMs to predict whether and to which degree DNA variants affect the binding of 1387 different human transcription factors. For each variant and transcription factor, the software combines the results of different models for a final prediction of the resulting binding-affinity change. The software is written in C++ for speed but variants can be entered through a web interface. Alternatively, a VCF file can be uploaded to assess variants identified by high-throughput sequencing. The search can be restricted to variants in the vicinity of candidate genes. FABIAN-variant is available freely at https://www.genecascade.org/fabian/.

Deep phenotyping: symptom annotation made simple with SAMS.

Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients' phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems: HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https://www.genecascade.org/SAMS/.

AutozygosityMapper: Identification of disease-mutations in consanguineous families.

With the shift from SNP arrays to high-throughput sequencing, most researchers studying diseases in consanguineous families do not rely on linkage analysis any longer, but simply search for deleterious variants which are homozygous in all patients. AutozygosityMapper allows the fast and convenient identification of disease mutations in patients from consanguineous pedigrees by focussing on homozygous segments shared by all patients. Users can upload multi-sample VCF files, including WGS data, without any pre-processing. Genome-wide runs of homozygosity and the underlying genotypes are presented in graphical interfaces. AutozygosityMapper extends the functions of its predecessor, HomozygosityMapper, to the search for autozygous regions, in which all patients share the same homozygous genotype. We provide export of VCF files containing only the variants found in homozygous regions, this usually reduces the number of variants by two orders of magnitude. These regions can also directly be analysed with our disease mutation identification tool MutationDistiller. The application comes with simple and intuitive graphical interfaces for data upload, analysis, and results. We kept the structure of HomozygosityMapper so that previous users will find it easy to switch. With AutozygosityMapper, we provide a fast web-based way to identify disease mutations in consanguineous families. AutozygosityMapper is freely available at https://www.genecascade.org/AutozygosityMapper/.

Biallelic truncating variants in ATP9A cause a novel neurodevelopmental disorder involving postnatal microcephaly and failure to thrive.

BACKGROUND: Genes implicated in the Golgi and endosomal trafficking machinery are crucial for brain development, and mutations in them are particularly associated with postnatal microcephaly (POM). METHODS: Exome sequencing was performed in three affected individuals from two unrelated consanguineous families presenting with delayed neurodevelopment, intellectual disability of variable degree, POM and failure to thrive. Patient-derived fibroblasts were tested for functional effects of the variants. RESULTS: We detected homozygous truncating variants in ATP9A. While the variant in family A is predicted to result in an early premature termination codon, the variant in family B affects a canonical splice site. Both variants lead to a substantial reduction of ATP9A mRNA expression. It has been shown previously that ATP9A localises to early and recycling endosomes, whereas its depletion leads to altered gene expression of components from this compartment. Consistent with previous findings, we also observed overexpression of ARPC3 and SNX3, genes strongly interacting with ATP9A. CONCLUSION: In aggregate, our findings show that pathogenic variants in ATP9A cause a novel autosomal recessive neurodevelopmental disorder with POM. While the physiological function of endogenous ATP9A is still largely elusive, our results underline a crucial role of this gene in endosomal transport in brain tissue.

Aviator: a web service for monitoring the availability of web services.

With Aviator, we present a web service and repository that facilitates surveillance of online tools. Aviator consists of a user-friendly website and two modules, a literature-mining based general and a manually curated module. The general module currently checks 9417 websites twice a day with respect to their availability and stores many features (frontend and backend response time, required RAM and size of the web page, security certificates, analytic tools and trackers embedded in the webpage and others) in a data warehouse. Aviator is also equipped with an analysis functionality, for example authors can check and evaluate the availability of their own tools or those of their peers. Likewise, users can check the availability of a certain tool they intend to use in research or teaching to avoid including unstable tools. The curated section of Aviator offers additional services. We provide API snippets for common programming languages (Perl, PHP, Python, JavaScript) as well as an OpenAPI documentation for embedding in the backend of own web services for an automatic test of their function. We query the respective APIs twice a day and send automated notifications in case of an unexpected result. Naturally, the same analysis functionality as for the literature-based module is available for the curated section. Aviator can freely be used at https://www.ccb.uni-saarland.de/aviator.

SIGLEC1 (CD169): a marker of active neuroinflammation in the brain but not in the blood of multiple sclerosis patients.

We aimed to evaluate SIGLEC1 (CD169) as a biomarker in multiple sclerosis (MS) and Neuromyelitis optica spectrum disorder (NMOSD) and to evaluate the presence of SIGLEC1+ myeloid cells in demyelinating diseases. We performed flow cytometry-based measurements of SIGLEC1 expression on monocytes in 86 MS patients, 41 NMOSD patients and 31 healthy controls. Additionally, we histologically evaluated the presence of SIGLEC1+ myeloid cells in acute and chronic MS brain lesions as well as other neurological diseases. We found elevated SIGLEC1 expression in 16/86 (18.6%) MS patients and 4/41 (9.8%) NMOSD patients. Almost all MS patients with high SIGLEC1 levels received exogenous interferon beta as an immunomodulatory treatment and only a small fraction of MS patients without interferon treatment had increased SIGLEC1 expression. In our cohort, SIGLEC1 expression on monocytes was-apart from those patients receiving interferon treatment-not significantly increased in patients with MS and NMOSD, nor were levels associated with more severe disease. SIGLEC1+ myeloid cells were abundantly present in active MS lesions as well as in a range of acute infectious and malignant diseases of the central nervous system, but not chronic MS lesions. The presence of SIGLEC1+ myeloid cells in brain lesions could be used to investigate the activity in an inflammatory CNS lesion.

MutationTaster2021.

Here we present an update to MutationTaster, our DNA variant effect prediction tool. The new version uses a different prediction model and attains higher accuracy than its predecessor, especially for rare benign variants. In addition, we have integrated many sources of data that only became available after the last release (such as gnomAD and ExAC pLI scores) and changed the splice site prediction model. To more easily assess the relevance of detected known disease mutations to the clinical phenotype of the patient, MutationTaster now provides information on the diseases they cause. Further changes represent a major overhaul of the interfaces to increase user-friendliness whilst many changes under the hood have been designed to accelerate the processing of uploaded VCF files. We also offer an API for the rapid automated query of smaller numbers of variants from within other software. MutationTaster2021 integrates our disease mutation search engine, MutationDistiller, to prioritise variants from VCF files using the patient's clinical phenotype. The novel version is available at https://www.genecascade.org/MutationTaster2021/. This website is free and open to all users and there is no login requirement.

Public data sources for regulatory genomic features.

High-throughput technologies have led to a continuously growing amount of information about regulatory features in the genome. A wealth of data generated by large international research consortia is available from online databases. Disease-driven studies provide details on specific DNA elements or epigenetic modifications regulating gene expression in specific cellular and developmental contexts, but these results are usually only published in scientific articles. All this information can be helpful in interpreting variants in the regulatory genome. This review describes a selection of high-profile data sources providing information on the non-coding genome, as well as pitfalls and techniques to search and capture information from the literature.

Novel sequencing technologies and bioinformatic tools for deciphering the non-coding genome.

High-throughput sequencing techniques have significantly increased the molecular diagnosis rate for patients with monogenic disorders. This is primarily due to a substantially increased identification rate of disease mutations in the coding sequence, primarily SNVs and indels. Further progress is hampered by difficulties in the detection of structural variants and the interpretation of variants outside the coding sequence. In this review, we provide an overview about how novel sequencing techniques and state-of-the-art algorithms can be used to discover small and structural variants across the whole genome and introduce bioinformatic tools for the prediction of effects variants may have in the non-coding part of the genome.